Digital communication techniques have been developed and implemented in communication systems, including communication systems utilizing radio channels. Digital communication techniques generally permit the communication system in which the techniques are implemented to achieve greater transmission capacity as contrasted to the capacity available with conventional analog communication techniques.
A communication system generally comprises a sending station and a receiving station communicating by way of one or more communication channels. Data to be communicated by the sending station to the receiving station is converted, if necessary, into a form to permit its transmission on the communication channel. A communication system can be defined by almost any combination of sending and receiving stations, including, for instance, circuit board-positioned sending and receiving elements as well as more conventionally-defined communication systems including users spaced at great distances apart communicating data between each other by transmission over radio channels.
When data transmitted on a communication channel is received at the receiving station, the receiving station acts upon, if necessary, the received data to recreate the informational content of the transmitted data. In an ideal communication system the data received at the receiving station is identical to the data transmitted by the sending station. However, in reality, much of the data may be distorted during its transmission on the communication channel. Such distortion distorts the data as received at the receiving station. If the distortion is significant, the informational content of portions of the data may not be recoverable.
A radio communication system is one example of a communication system utilized to transmit data between sending and receiving stations. In a radio communication system, the communication channel is formed of a radio communication channel. A radio communication channel may be defined within a portion of the electromagnetic spectrum. In a wireline communication system, in contrast, a physical connection between the sending and receiving stations is implemented to form the communication channel. Transmission of data upon a radio communication channel is particularly susceptible to distortion, due in part to the propagation characteristics of the radio communication channel. Data communicated on conventional wireline channels are also, however, susceptible to distortion in manners analogous to the manner by which distortion is introduced upon the data transmitted in a radio communication system.
In a communication system, which utilizes digital communication techniques, information, which is to be communicated, is digitized to form digital bits. The digital bits are typically formatted according to a formatting scheme. Groups of the digital bits, for example, are assembled to form a packet of data.
Orthogonal Frequency Division Multiplexing (OFDM) is a method that allows transmitting high data rates over extremely degraded channels at a comparable low complexity. In the classical terrestrial broadcasting scenario, in contrast to, for example, satellite communications where we have one single direct path from transmitter to receiver, we have to deal with a multipath-channel as the transmitted signal arrives at the receiver along various paths of different length. Since multiple versions of the signal interfere with each other (inter symbol interference (ISI)) it becomes very difficult to extract the original information. The common representation of the multipath channel is the channel impulse response (cir) of the channel, which is the signal received at the receiving station if a single pulse is transmitted from the transmitter.
If we assume a system transmitting discrete information in time intervals T, the critical measure concerning the multipath-channel is the delay Tm of the longest path with respect to the earliest path. A received symbol can theoretically be influenced by Tm/T previous symbols. This influence has to be estimated and compensated for in the receiver, a task that may become very challenging.
Multi-path transmission of the data upon a radio channel or other communication channel introduces distortion upon the data as the data is actually communicated to the receiving station by a multiple number of paths. The data detected at the receiving station, therefore, is the combination of signal values of data communicated upon a plurality of communication paths. Intersymbol interference and Rayleigh fading causes distortion of the data. Such distortion, if not compensated for, prevents the accurate recovery of the transmitted data.
Various methods are used to compensate for the distortion introduced in the data during its transmission upon a communication path.
The ability to obtain reliable channel estimates affects the system performance considerably. A common way of estimating the channel in TDMA (time division multiple access) is to transmit a training sequence and evaluate a Least square (LS) estimate of the channel at the receiver based on the knowledge of the training sequence. The LS channel estimate is basically a noisy version of the exact channel estimate. Hence, this technique relies on a law noise environment. Simulations show that for an uncoded system, a gap of about three dB at BER floor of 0.01 exists when using the LS channel estimate in comparison to using the exact channel estimate. This points to the advantages of using interpolation coefficients (with the least possible complexity) to enhance the LS channel estimate.
The correlation properties of the channel have been used to enhance the LS estimate. For example in the paper authored by J. J. Vands Beek, O. Edfors, M. Sandell, S. K. Wilson, and P. O. Borjeson, “On Channel Estimation in OFDM systems,” in proc. 45th IEEE on Vehicular Technology Conference, IL, July 1995, pp. 815-819, time correlation is used for channel estimate enhancement. A time interpolator relies on the correlation between different channel taps in the time domain, which requires the knowledge of the channel statistics versus time. The technique requires calculating the interpolator for every transmission burst. The interpolator requires a matrix inversion of dimension N (the size of the training sequence) for every burst which increases the system complexity.
In the paper authored by J. J. Vande Beek, O. Edfors, M. Sandell, S. K. Wilson, and P. O, Borieson, “OFDM Channel Estimation with Singular Value Decomposition,” in proc. 46th IEEE on Vehicular Technology Conference, Atlanta, Ga., April 1996, pp. 923-927, interpolation in the frequency domain is used to enhance the LS estimate. This technique suffers from increased complexity due to the requirement of a matrix inversion. This technique was modified to include low rank approximation in the interpolator to decrease complexity, however, the modified technique requires estimation of a group of dominant eigenvalues and eigenvectors for every transmission burst. Since performing such eigendecomposition is a complex task, the modified technique suffers from complexity as well.
In the paper authored by Y. Li, L. J. Cimini, Jr. and N. R. Sollenberger, “Robust Channel Estimation for OFDM Systems with Rapid Dispersive Fading Channels,” IEEE Trans. On Communications, vol. 46, No. 7, July 1998, both the time and frequency channel statistics are used for interpolation. While reliance on both statistics enhances the channel estimate, it requires the knowledge of both time and frequency statistics for every transmission burst. In addition, calculations must be performed by the interpolator for every burst. Determining the channel statistics, every burst is also a very difficult task. This technique also requires additional processing capacity at the receiver to estimate the channel statistics from the received signal. This in turn increases the complexity of the receiver.
In the paper authored by Y. Li, N. Seshadri and S. Ariyavisitakul, “Channel Estimation for OFDM Systems with Transmitter Diversity in Mobile Wireless Channels,” IEEE JSAC, vol. 17, No. 3, March 1999, a channel estimate for space time coding (STC) was introduced that basically evaluates the LS estimate of the channel in the time domain without doing any interpolation to avoid relying on the channel statistics. While the LS estimate alone without interpolation suffers from noise, in the presence of more than one transmitting antenna, it will also suffer from interference.
In the paper authored by S. K. Wilson, R. E. Khayata and J. M. Cioffi, “16 QAM Modulation with Orthogonal Frequency Division Multiplexing in a Rayleigh-Fading Environment,” in proc. VTC-1994, pp. 1660-1664, Stockholm, Sweden, June 1994, a different approach for fast fading channels was introduced. This approach relies on adaptive interpolation. Use of this adaptive algorithm incurs problems related to algorithm convergence, i.e., the eigenvalue spread of the received data.
Such impairments as described above hinder the implementation of the LS channel estimator in real time applications.